Weather detection method, system and storage medium based on meteorological rain radar

Abstract

The present invention discloses a weather detection method, system and storage medium based on a meteorological rain measuring radar, comprising a meteorological rain measuring radar, a weather data set control and management center and a radar time series graph neural network detection algorithm arranged in the weather data set control and management center; the method collects weather with different cloud layer characteristics by means of a meteorological rain measuring radar, and inputs the obtained electromagnetic wave scattering parameters of the weather with different cloud layer characteristics into the radar time series graph neural network detection algorithm for analysis and calculation, so as to obtain the polarization phase difference corresponding to the weather with different cloud layer characteristics to detect the height and vertical distribution of the cloud layer; when the height and vertical distribution of the cloud layer exceed the boundary threshold, risk warning information of different weather types is issued, and the spectrum width and corresponding polarization phase difference of the weather with different cloud layer characteristics are automatically detected; the present invention improves the accuracy and efficiency of weather detection by integrating multi-parameter collaborative analysis, multi-modal data modeling and real-time warning strategy.

Description

Technical Field

[0001] The present invention relates to the field of weather detection with different cloud layer characteristics, and in particular to a weather detection method, system and storage medium based on meteorological rain measuring radar. Background Art

[0002] Weather detection methods based on meteorological precipitation radar are an indispensable technical tool in modern meteorological observation. By transmitting electromagnetic waves and receiving reflected echoes from target objects, they analyze parameters such as the reflectivity, Doppler velocity, and polarization characteristics of precipitation particles (such as raindrops, hail, and snowflakes) to monitor weather conditions in real time. This technology plays a vital role in short-term precipitation forecasting, rainstorm warnings, and extreme weather monitoring. However, with the increasing complexity of meteorological systems and the diversification of weather phenomena, the detection capabilities of a single radar system can no longer fully meet demand. Current weather detection methods rely heavily on single radar or single data sources for analysis, failing to fully leverage the potential of multimodal data fusion and historical meteorological data to improve forecast accuracy. This has become a key shortcoming limiting the further development of existing technologies.

[0003] Current meteorological rainfall radar technology suffers from significant deficiencies in analytical accuracy, primarily due to insufficient integration of multimodal data. Meteorological phenomena inherently possess complex, multidimensional characteristics, with strong correlations between different radar parameters (such as reflectivity, Doppler velocity, and polarization characteristics) and other observational data (such as weather station records, satellite remote sensing data, and wind profiler radar data). However, existing technologies typically process radar data in isolation, ignoring the necessity of multimodal data fusion. The lack of effective collaborative analysis of multi-source information exacerbates the limitations of single radar data in complex weather conditions. For example, in severe convective weather, the irregular shape and motion of precipitation particles can lead to inconsistencies between polarization parameters and velocity data in radar data, making it difficult to accurately describe the overall characteristics of the weather system using single-data analysis. Furthermore, existing analysis methods rely primarily on real-time data and fail to fully utilize historical data for comparative analysis. This leads to a limited understanding of weather trends and compromises early warning capabilities for extreme weather events.

[0004] Another major drawback is the low utilization of historical meteorological data, which fails to integrate historical weather patterns and trends into a more comprehensive analysis of current weather data. Historical data contains a wealth of statistical regularities and characteristic patterns regarding weather evolution. Combining multimodal real-time data with historical data for correlation analysis would significantly improve the accuracy of early warnings. However, most existing technologies lack the ability to mine historical data and are unable to exploit past data patterns to correct errors in real-time observations. For example, the formation of certain localized weather phenomena (such as tornadoes and hail) may be associated with specific weather patterns, which can only be identified through analysis of historical data. Without this correlation information, radar observations may underestimate the intensity or probability of certain extreme weather events. Overall, existing meteorological rainfall radar technology fails to effectively integrate multimodal and historical data, resulting in inaccurate radar data analysis and limiting its effectiveness in complex weather conditions. Summary of the Invention

[0005] In order to overcome the above-mentioned deficiencies in the prior art, the present invention provides a weather detection method, system and storage medium based on a meteorological rain measuring radar to solve the above-mentioned problems.

[0006] In order to achieve the above-mentioned purpose, the technical solution provided by the preferred embodiment of the present invention is as follows: In terms of method, the preferred embodiment of the present invention provides a weather detection method based on a meteorological rain measuring radar, the method including a meteorological rain measuring radar, a weather data set control and management center that interacts with the meteorological rain measuring radar for data, and a radar timing graph neural network detection algorithm arranged in the weather data set control and management center; the method includes: using the meteorological rain measuring radar to collect electromagnetic wave scattering parameters of weather with different cloud layer characteristics; inputting the obtained electromagnetic wave scattering parameters of weather with different cloud layer characteristics into the radar timing graph neural network detection algorithm for analysis and calculation to obtain the spectral width and corresponding polarization phase difference of weather with different cloud layer characteristics; combining the influencing factors of the electromagnetic wave scattering parameters and the parameter boundary condition judgment results to perform polarization phase difference judgment in the big data model, and if the polarization phase difference fluctuation range is higher than the safety limit interval, issuing different weather type risk warning information.

[0007] In the present invention, before the above-mentioned step of inputting the acquired electromagnetic wave scattering parameters of different cloud layer characteristic weather into the radar time series graph neural network detection algorithm for analysis and calculation, the method includes: integrating the electromagnetic wave scattering parameter sets of different cloud layer characteristic weather, including electromagnetic wave scattering parameters of different unit times, cloud particle types of different cloud layer characteristic weather corresponding to each of the electromagnetic wave scattering parameters of the unit time, and the cloud particle types corresponding to the echo intensities and polarization parameters of different cloud layer characteristic weather.

[0008] The electromagnetic wave scattering parameter set is used, a convolutional neural network model is adopted to optimize the radar time series graph neural network detection algorithm, and the model is used to analyze the influencing factors of the electromagnetic wave scattering parameters and judge the parameter boundary conditions.

[0009] In the present invention, the above-mentioned step of inputting the acquired electromagnetic wave scattering parameters of different cloud layer characteristic weather into the radar time sequence graph neural network detection algorithm for analysis and calculation includes: matching the electromagnetic wave scattering parameter characteristics of the electromagnetic wave scattering parameters of the different cloud layer characteristic weather with the electromagnetic wave scattering parameter characteristics of the unit time electromagnetic wave scattering parameters in the electromagnetic wave scattering parameter set in the radar time sequence graph neural network detection algorithm to obtain the compliance rate of the electromagnetic wave scattering parameters of the different cloud layer characteristic weather and the electromagnetic wave scattering parameters per unit time; selecting the electromagnetic wave scattering parameter per unit time with the largest compliance rate in the electromagnetic wave scattering parameter set, and using the cloud particle type of the selected electromagnetic wave scattering parameter per unit time as the actual monitoring value of the electromagnetic wave scattering parameter of the different cloud layer characteristic weather to obtain the spectral width of different cloud layer characteristic weather and the polarization parameter of different cloud layer characteristic weather.

[0010] In the present invention, the above-mentioned step of inputting the obtained electromagnetic wave scattering parameters of different cloud layer characteristic weather into the radar time series graph neural network detection algorithm for analysis and calculation includes: statistically analyzing the echo ratios corresponding to the polarization parameters of different cloud layer characteristic weather collected by the meteorological rain measuring radar within a preset time length.

[0011] In the present invention, the above-mentioned step of judging the polarization parameters and issuing risk warning information of different weather types when the fluctuation range of the polarization phase difference is higher than the safety limit interval includes: the height of the cloud layer, vertical distribution data and structural changes and development data of the cloud layer in different terrains and altitudes, calculating the characteristic weather of different cloud layers in the region according to the regional information and polarization parameters, and issuing risk warning information of different weather types, wherein the risk warning information of different weather types includes rainfall probability, thunderstorm probability, climate temperature and humidity, haze probability and level, secondary comprehensive disaster probability and level, wherein different probabilities and levels correspond to different degrees of severe convective weather hazards.

[0012] In the present invention, the above-mentioned method for detecting weather with different cloud layer characteristics also includes an edge computing gateway server, and the edge computing gateway server and the weather data control management center use 5G signals to exchange data. The edge computing gateway server is provided with the weather analysis and early warning model of the multimodal fusion weight analysis. The step of using the meteorological rain measuring radar to collect electromagnetic wave scattering parameters of weather with different cloud layer characteristics in the monitoring area, which are used as electromagnetic wave scattering parameters of weather with different cloud layer characteristics obtained by the method for detecting weather with different cloud layer characteristics, includes: the meteorological rain measuring radar uploads the collected electromagnetic wave scattering parameters of weather with different cloud layer characteristics in the monitoring area to the edge computing gateway server.

[0013] Furthermore, the weather analysis and warning model of the multimodal fusion weight analysis is expressed as follows:

[0014] ;

[0015] in, Represents the weight of factors affecting different electromagnetic wave scattering parameters, Indicates the echo strength, represents the polarization parameter, Represents the differential reflectivity of weather with different cloud characteristics, represents parameter boundary conditions, Cloud particle characteristic parameter matrix, represents polarization error compensation, LN is layer normalization, Dropout( ) is the preset weight distribution calculation, represents the multi-head self-attention mechanism, , , They are weather embedding matrices of different cloud layer characteristics, corresponding to query, key and value, is the learnable matrix of multi-head self-attention at different unit times.

[0016] In the present invention, the above-mentioned step of inputting the obtained electromagnetic wave scattering parameters of weather with different cloud layer characteristics into the radar time series graph neural network detection algorithm for analysis and calculation includes: the edge computing gateway server inputs the obtained electromagnetic wave scattering parameters of weather with different cloud layer characteristics in the monitoring area into the radar time series graph neural network detection algorithm of the edge computing gateway server to detect and count the electromagnetic wave scattering parameters of weather with different cloud layer characteristics in the monitoring area.

[0017] As for the system, a preferred embodiment of the present invention provides a weather detection system based on a meteorological rain measuring radar, the system including a meteorological rain measuring radar, a weather data set control and management center that exchanges data with the meteorological rain measuring radar, and different weather type risk warning modules connected to the weather data set control and management center. The meteorological rain measuring radar is used to collect electromagnetic wave scattering parameters of different cloud layer characteristic weather in the monitoring area, which are used as electromagnetic wave scattering parameters of different cloud layer characteristic weather obtained by the risk warning method for detecting different weather types with different cloud layer characteristic weather; the weather data set control and management center includes a radar time series graph neural network detection algorithm, and the different weather type risk warning modules include: an electromagnetic wave scattering parameter interface, which is used to input the obtained electromagnetic wave scattering parameters of different cloud layer characteristic weather into the radar time series graph neural network detection algorithm for analysis and calculation to obtain the spectral width and corresponding polarization phase difference of different cloud layer characteristic weather; a different weather type risk warning judgment interface, which is used to judge the polarization parameter, and when the fluctuation range of the polarization phase difference is higher than the safety limit interval, different weather type risk warning information is issued.

[0018] In the present invention, the above-mentioned different weather type risk warning module also includes: an electromagnetic wave scattering parameter acquisition interface, which is used to integrate the electromagnetic wave scattering parameter sets of different cloud layer characteristic weather, including electromagnetic wave scattering parameters of different unit times, each of the electromagnetic wave scattering parameters per unit time includes a single electromagnetic wave scattering parameter of a different cloud layer characteristic weather and the corresponding cloud particle type of the different cloud layer characteristic weather, and the cloud particle type corresponds to the echo intensity and polarization parameters of the different cloud layer characteristic weather.

[0019] The weather analysis and warning model establishment and judgment interface is used to use the electromagnetic wave scattering parameter set, adopt a convolutional neural network model to optimize the radar time series graph neural network detection algorithm, and use this model to analyze the influencing factors of electromagnetic wave scattering parameters and judge the parameter boundary conditions.

[0020] In the present invention, the above-mentioned different weather type risk warning judgment interface is also used for: the height of the cloud layer, vertical distribution data and the structural changes and development data of the cloud layer in the workshop area at different terrains and altitudes, and the cumulative number and incremental number of different cloud layer characteristic weather in the area are calculated based on regional information and polarization parameters, and different weather type risk warning information is issued, wherein the different weather type risk warning information includes rainfall probability, thunderstorm probability, climate temperature and humidity, haze probability and level, secondary comprehensive disaster probability and level, wherein different probabilities and levels correspond to different degrees of severe convective weather hazards.

[0021] The present invention also provides a storage medium storing a computer program. When the computer program is run by a processor, the computer program executes the steps of the weather detection method based on the meteorological rainfall radar.

[0022] Compared with the prior art, this application has at least one of the following beneficial technical effects:

[0023] 1. This method utilizes the electromagnetic wave scattering parameters of different cloud layer characteristics in the monitoring area, collected by meteorological rainfall radar, as the electromagnetic wave scattering parameters for different cloud layer characteristics acquired by the weather detection method. These electromagnetic wave scattering parameters are then input into the radar time series graph neural network detection algorithm for analysis and calculation to obtain the spectral width and corresponding polarization phase difference of different cloud layer characteristics. The spectral width and corresponding polarization phase difference of different cloud layer characteristics are detected by the weather data monitoring and management center, simplifying the analysis and calculation of different cloud layer characteristics. This method is an efficient and accurate modern meteorological monitoring method with the significant advantages of multi-dimensional fusion, intelligent analysis, real-time response, and wide adaptability.

[0024] 2. This method comprehensively reflects the physical properties and dynamic evolution of different cloud-related weather characteristics by collecting electromagnetic wave scattering parameters, including echo intensity, polarization parameters, differential reflectivity, and other data. Compared with traditional single-parameter analysis, this method introduces multimodal data fusion technology, combining cloud particle types, cloud fluctuation range, and environmental boundary conditions to accurately capture the core characteristics of complex weather phenomena. Secondly, this method uses a multi-head self-attention mechanism to dynamically assign weights and perform deep modeling and correlation analysis on cloud characteristic data in the spatiotemporal dimensions. This mechanism can not only highlight key weather characteristics and capture subtle changes in time series, but also enhance the precise understanding of spatial distribution characteristics, providing a reliable basis for risk warnings of severe convective weather such as thunderstorms, hail, and heavy rain.

[0025] 3. By introducing layer normalization and polarization error compensation techniques, the robustness to observational data noise and computational stability are greatly improved. Normalization reduces the interference of different data scales on model performance, while error compensation effectively eliminates systematic errors caused by equipment deviations, making the results more accurate and reliable. Combining a big data model with the influencing factors of electromagnetic wave scattering parameters and the results of parameter boundary conditions, this method can set reasonable fluctuation thresholds based on historical distribution parameters and use deviation metrics to determine whether current cloud characteristics exceed safe ranges. This big data-driven analytical framework not only enhances the model's timeliness and adaptability, but also dynamically adjusts warning levels based on real-time observation data, significantly improving the accuracy of extreme weather forecasts. Furthermore, this method is highly scalable and universal, adapting to the warning needs of different weather types (such as heavy rain, strong winds, and typhoons).

[0026] 4. Through a flexible embedding matrix and weight distribution mechanism, the model's focus can be adjusted based on the characteristics of the target weather phenomenon, thereby specifically improving detection effectiveness. By integrating multi-parameter collaborative analysis, multimodal data modeling, and real-time warning strategies, this method not only achieves new heights in weather detection accuracy and efficiency, but also provides strong technical support for improving meteorological disaster monitoring capabilities and reducing disaster risks. This combined advantage makes it a valuable tool for addressing complex meteorological conditions and extreme weather, offering broad application prospects in meteorological services, public safety, and disaster prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope of the present invention. Those skilled in the art can also derive other relevant drawings based on these drawings without inventive effort.

[0028] Figure 1 is a flow chart of the steps of the method of the present invention;

[0029] Figure 2 It is a module composition diagram of the system of the present invention;

[0030] Figure 3 This is a diagram showing the composition of the risk warning modules for different weather types of the present invention. DETAILED DESCRIPTION

[0031] It should be noted that, unless there is a conflict, the embodiments in this application and the features described in the embodiments can be combined with each other. The application is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] like Figure 1 As shown, in this embodiment, the weather detection method based on the meteorological rain measuring radar includes the following steps:

[0033] Step S1, using a meteorological rainfall radar to collect characteristic weather electromagnetic wave scattering parameters of different cloud layers in the monitoring area;

[0034] Step S2, integrating electromagnetic wave scattering parameter sets for different cloud layer characteristic weather conditions, including electromagnetic wave scattering parameters for different unit times, cloud particle types for different cloud layer characteristic weather conditions corresponding to each electromagnetic wave scattering parameter for each unit time, echo intensities and polarization parameters corresponding to the cloud particle types for different cloud layer characteristic weather conditions, and differential reflectivities corresponding to the polarization parameters for different cloud layer characteristic weather conditions;

[0035] Step S3, using the electromagnetic wave scattering parameter set, establishing a weather analysis and warning model for multimodal fusion weight analysis, and using the model to analyze the influencing factors of the electromagnetic wave scattering parameters and determine the boundary conditions of the parameters;

[0036] Step S4: inputting the obtained electromagnetic wave scattering parameters of different cloud layer characteristic weather into the radar time series graph neural network detection algorithm for analysis and calculation to obtain the spectrum width and corresponding polarization phase difference of different cloud layer characteristic weather;

[0037] Step S5: combining the influencing factors of electromagnetic wave scattering parameters and the judgment results of parameter boundary conditions to perform polarization phase difference judgment in the big data model. If the polarization phase difference fluctuation range is higher than the safety limit range, a risk warning information of different weather types is issued;

[0038] In this embodiment, the electromagnetic wave scattering parameter set for different cloud layer weather characteristics may include electromagnetic wave scattering parameters for different periods and seasons of the aforementioned different cloud layer weather characteristics in the monitoring area (i.e., the electromagnetic wave scattering parameters per unit time). The cloud particle type may include the spectral width and polarization parameters for the aforementioned different cloud layer weather characteristics. The number of electromagnetic wave scattering parameters per unit time in the electromagnetic wave scattering parameter set may be set based on specific circumstances and is not specifically limited herein.

[0039] In this embodiment, the electromagnetic wave scattering parameter set can be calculated using a weather analysis and warning model using a multimodal fusion weighted analysis to analyze the radar time series graph neural network detection algorithm. This model is used to analyze the factors affecting the electromagnetic wave scattering parameters and determine the parameter boundary conditions. Understandably, the electromagnetic wave scattering parameter set is calculated to obtain electromagnetic wave scattering parameter characteristics corresponding to different cloud characteristics under different polarization parameters.

[0040] The weather data set control management center or the edge computing gateway server can be configured with a radar time series graph neural network detection algorithm. Of course, the weather data set control management center and the edge computing gateway server can also be configured with a radar time series graph neural network detection algorithm to detect electromagnetic wave scattering parameters of weather with different cloud layer characteristics. If the weather data set control management center is used to detect electromagnetic wave scattering parameters of weather with different cloud layer characteristics, the computational load of the edge computing gateway server can be shared to reduce the load on the edge computing gateway server. In addition, the weather data set control management center directly detects electromagnetic wave scattering parameters of weather with different cloud layer characteristics, reducing the upload of electromagnetic wave scattering parameter data of weather with different cloud layer characteristics, which can increase the speed of analysis and calculation.

[0041] In this embodiment, when integrating electromagnetic wave scattering parameters of weather with different cloud layer characteristics, they can be directly obtained manually, or they can be obtained using equipment, tools, etc. Specifically, for example, relevant personnel go to the production workshop to use a meteorological rain measuring radar to shoot electromagnetic wave scattering parameters of weather with different cloud layer characteristics, and then the meteorological rain measuring radar uploads the obtained electromagnetic wave scattering parameters of weather with different cloud layer characteristics to the edge computing gateway server through the first data interaction interface. The method of obtaining electromagnetic wave scattering parameters is not specifically limited here. The obtained electromagnetic wave scattering parameters of weather with different cloud layer characteristics only include different cloud layer characteristics per unit time, so as to detect different weather type risk warnings for such different cloud layer characteristics. If the electromagnetic wave scattering parameters of weather with different cloud layer characteristics include different cloud layer characteristics for multiple time periods, the electromagnetic wave scattering parameters can be divided so that each electromagnetic wave scattering parameter of weather with different cloud layer characteristics includes different cloud layer characteristics per unit time.

[0042] In this embodiment, the weather analysis and warning model of the multimodal fusion weight analysis is expressed as follows:

[0043] ;

[0044] in, Represents the weight of factors affecting different electromagnetic wave scattering parameters, Indicates the echo strength, represents the polarization parameter, Represents the differential reflectivity of weather with different cloud characteristics, represents parameter boundary conditions, Cloud particle characteristic parameter matrix, represents polarization error compensation, LN is layer normalization, Dropout( ) is the preset weight distribution calculation, represents the multi-head self-attention mechanism, , , They are weather embedding matrices of different cloud layer characteristics, corresponding to query, key and value, is the learnable matrix of multi-head self-attention at different unit times.

[0045] In other embodiments, the collected electromagnetic wave scattering parameters of different cloud layer characteristic weather do not need to be uploaded to the edge computing gateway server. The collected electromagnetic wave scattering parameters can be directly input into the radar time series graph neural network detection algorithm in the weather data monitoring management center for statistical detection, and then the occurrence period of different cloud layer characteristic weather is predicted, and different weather type risk warning information is issued according to the occurrence period.

[0046] The radar time series graph neural network detection algorithm can be used in parallel to simultaneously perform statistical detection of electromagnetic wave scattering parameters for different cloud layer characteristics to improve detection efficiency. The radar time series graph neural network detection algorithm can be used to analyze the electromagnetic wave scattering parameter characteristics of electromagnetic wave scattering parameters for different cloud layer characteristics. The extracted electromagnetic wave scattering parameter characteristics are matched with the electromagnetic wave scattering parameter characteristics obtained above to achieve analysis and calculation of different cloud layer characteristics.

[0047] Furthermore, the electromagnetic wave scattering parameter characteristics of the electromagnetic wave scattering parameters of the different cloud layer characteristic weather are matched with the electromagnetic wave scattering parameter characteristics of the electromagnetic wave scattering parameters per unit time in the electromagnetic wave scattering parameter set in the radar time series graph neural network detection algorithm to obtain the compliance rate of the electromagnetic wave scattering parameters of the different cloud layer characteristic weather and the electromagnetic wave scattering parameters per unit time; the electromagnetic wave scattering parameter per unit time with the largest compliance rate is selected from the electromagnetic wave scattering parameter set, and the cloud particle type of the selected electromagnetic wave scattering parameter per unit time is used as the actual monitoring value of the electromagnetic wave scattering parameter of the different cloud layer characteristic weather to obtain the spectral width and polarization parameter of the different cloud layer characteristic weather.

[0048] Furthermore, the echo ratios corresponding to the polarization parameters of different cloud layer characteristic weather in the monitoring area collected by the meteorological rain measuring radar within a preset time period are counted. The preset time period can be set according to the specific situation. By inputting the electromagnetic wave scattering parameters of different cloud layer characteristic weather into the radar time series graph neural network detection algorithm, efficient and accurate analysis and calculation are performed. This method makes full use of the multi-parameter observation capabilities of the meteorological rain measuring radar, especially the key indicators such as the polarization parameters and echo ratios of different cloud layer characteristic weather in the monitoring area within a predetermined time period, and combines the time series graph neural network model to conduct in-depth mining and analysis of the data. Through the input radar data, the model can capture the dynamic evolution of cloud layer characteristic weather in the time and space dimensions, and at the same time associate the interaction of different electromagnetic wave scattering parameters to generate more accurate weather analysis results.

[0049] The core steps include: First, using meteorological rainfall radar equipment, electromagnetic wave scattering parameters, including polarization parameters, differential reflectivity, echo intensity, and Doppler velocity, are collected within a specific timeframe within the monitoring area. These parameters characterize cloud particle characteristics (such as shape, size, and number) as well as the distribution dynamics of precipitation particles. Next, these data are fed into a radar time-series graph neural network detection algorithm. Leveraging the neural network's spatiotemporal modeling capabilities, feature learning and comprehensive analysis are performed on both radar time-series data and graph-structured data. In particular, statistical analysis of the relationship between polarization parameters and echo ratio provides a basis for the classification and dynamic monitoring of cloud weather systems. Furthermore, by correlating different time-series data within the radar monitoring area, the model accurately predicts cloud dynamic characteristics.

[0050] A significant advantage of this method lies in its ability to effectively fuse multimodal data and capture spatiotemporal features using a radar time-series neural network model, addressing the inadequate capture of dynamic weather changes in traditional meteorological observations. Furthermore, the introduction of polarization parameters and echo ratios enables more precise monitoring of complex cloud characteristics (such as convective weather, thunderstorms, and hail), providing scientific support for early warning of extreme weather events. This method has demonstrated broad application in areas such as heavy rain monitoring, hail warning, and thunderstorm tracking.

[0051] The graph neural network detection algorithm includes:

[0052] (1) Data input and preprocessing, assuming that radar observation data Include time points, the data at each time point is a feature vector, and the data can be constructed as a graph structure:

[0053] ;

[0054] in, Indicates the The feature vector at time, is the feature dimension. The radar data is constructed as a time series graph ,in represents a set of nodes (corresponding to time points), Represents the connection relationship between time points (adjacent relationship in time or space).

[0055] (2) Graph Neural Network Modeling, 2.1 Adjacency Matrix Definition, Constructing the Adjacency Matrix of Radar Observations :

[0056] ;

[0057] 2.2 Graph Convolution Operation,The update rules of the graph convolution layer are as follows:

[0058] ;

[0059] in: It is The node feature matrix of the layer; It is The weight matrix of the layer; is the bias term; is the activation function (such as ReLU). Initial input features The time series characteristics of the radar .

[0060] (3) Time Series Modeling, 3.1 Time Series Feature Extraction (LSTM / GRU), perform time series modeling on the features output by the graph neural network, and use the LSTM network to extract time-dependent features:

[0061] ;

[0062] in: It is Graph convolution features at each moment; is the hidden state output by the LSTM.

[0063] 3.2 Temporal feature aggregation: Aggregate the hidden states extracted by LSTM in the time direction:

[0064] ;

[0065] in, It is the aggregated time series feature.

[0066] (4) Multimodal fusion and classification, 4.1 Feature fusion, the spatial features output by the graph neural network Time features with LSTM output Fusion:

[0067] ;

[0068] Among them, Concat represents the feature concatenation operation.

[0069] 4.2 Classification and prediction, use the fully connected layer to classify the fusion features:

[0070] ;

[0071] in, is the type of weather being predicted (e.g., heavy rain, hail, etc.); and are the weights and bias terms of the fully connected layer.

[0072] (5) Risk assessment of polarization parameters: Combine historical data and the upper and lower bounds of polarization parameters, and use conditional discriminant functions to perform risk assessment:

[0073] ;

[0074] in, is the polarization parameter detected by the current radar; is the preset risk threshold, It is an indicator function used to determine whether it exceeds the safety range.

[0075] This paper uses a graph neural network detection algorithm to simultaneously capture the temporal dynamics and spatial structure of radar data, improving the accuracy of forecasts for complex weather phenomena. By integrating big data models with neural networks, it is suitable for meteorological forecasting tasks that require high real-time performance and precision.

[0076] The method combines the electromagnetic wave scattering parameter influencing factors and the parameter boundary condition judgment results to perform polarization phase difference judgment in the big data model, and issues risk warning information of different weather types if the polarization phase difference fluctuation range is higher than the safety limit range; including:

[0077] (a) A historical statistical model of polarization parameters. Based on the influencing factors of electromagnetic wave scattering parameters and the judgment results of parameter boundary conditions, a statistical distribution model of polarization parameters is established to represent the parameter fluctuation range under certain weather types.

[0078] Assume that the polarization parameter set is ,in For the The polarization parameter value of the observation.

[0079] Calculate historical mean of polarization parameters and standard deviation :

[0080] ;

[0081] Determine the safe fluctuation range of a certain weather type based on historical data ,in It is a parameter that controls the safety range (usually 2 or 3 to represent a 95% or 99% confidence interval).

[0082] (b) Real-time polarization phase difference fluctuation judgment, real-time polarization phase difference fluctuation range judgment formula:

[0083] Assume that the polarization parameter value at the current moment is , to determine whether it exceeds the safety limit range:

[0084] ;

[0085] in, Indicates the degree of deviation of polarization parameters.

[0086] Conditional judgment:

[0087] ;

[0088] when , it means that the current polarization parameters are out of the safe range and an early warning needs to be triggered.

[0089] (c) Combining multi-parameter big data model calculations. In practical applications, big data models will combine multiple polarization parameters (such as Assume that multiple polarization parameters constitute a feature vector ,in, For the polarization parameters.

[0090] Calculate the deviation of the real-time polarization parameter vector from the historical distribution:

[0091] ;

[0092] in: and For the The historical mean and standard deviation of each polarization parameter. Represents the normalized Euclidean distance of the integrated polarization parameters.

[0093] Conditional judgment:

[0094] ;

[0095] (d) Weather type risk warning formula: Based on the polarization phase difference fluctuation and historical weather data, the risk is classified into different levels and the corresponding warning is triggered: Let the polarization phase difference risk weight corresponding to each weather type be and impact factor ,in Indicates the weather type (such as heavy rain, hail, etc.).

[0096] Calculating a comprehensive risk score :

[0097] ;

[0098] in: Indicates the impact weight of weather type (e.g. if the risk of heavy rain is high, a higher value). Indicates the relevance weight for a specific weather type.

[0099] Comprehensive judgment warning level:

[0100] ;

[0101] in, and is the threshold of the warning level.

[0102] like Figure 2 and Figure 3As shown, a weather detection system based on a meteorological rain measuring radar comprises: a meteorological rain measuring radar, a weather data set control and management center for data interaction with the meteorological rain measuring radar, and different weather type risk warning modules connected to the weather data set control and management center. The different weather type risk warning modules comprise: a radar electromagnetic wave scattering parameter analysis interface, an initial information comprehensive preprocessing interface, a different weather type risk warning judgment interface, an electromagnetic wave scattering parameter acquisition interface, and a weather analysis warning model establishment and judgment interface.

[0103] Weather comprehensive management personnel can query historical statistical detection records and risk warning information records of different weather types through the weather data control management center. In addition, weather comprehensive management personnel can query the current proportion of polarization parameters of different cloud layer characteristic weather in different regions and corresponding echo ratios and other risk warning information of different weather types per unit time through the weather data control management center. Based on the above design, the method provided by the present invention can replace the manual detection of spectral width and polarization parameters of different cloud layer characteristic weather by the edge computing gateway server or the weather data control management center, simplifying the steps of detecting different cloud layer characteristic weather and improving the detection accuracy. This method collects and integrates electromagnetic wave scattering parameters, combines multimodal information such as polarization parameters, differential reflectivity, and cloud particle characteristic matrix, and uses a multi-head self-attention mechanism to dynamically allocate weights, thereby realizing accurate modeling and analysis of spatiotemporal characteristics. In addition, this method introduces a historical big data model to determine the parameter fluctuation range, thereby improving the detection capability and warning accuracy of extreme weather. It has the advantages of strong robustness, high adaptability, and good scalability, and can be applied to real-time risk assessment and warning of various complex weather types.

[0104] In this embodiment, the method for detecting different weather types based on different cloud layer characteristics and providing risk warnings for different weather types may include a meteorological rainfall radar, a weather data monitoring and management center, and a risk warning module for different weather types. The meteorological rainfall radar and the weather data monitoring and management center utilize 5G signals for data exchange, and are configured to upload collected electromagnetic wave scattering parameters for weather with different cloud layer characteristics in the monitoring area to the weather data monitoring and management center for analysis and calculation of the electromagnetic wave scattering parameters for weather with different cloud layer characteristics, and to issue risk warning information for different weather types when the detection results exceed a boundary threshold.

[0105] The different weather type risk warning module may include a radar electromagnetic wave scattering parameter analysis interface and a different weather type risk warning judgment interface. In other embodiments, the different weather type risk warning module may also include an electromagnetic wave scattering parameter acquisition interface and a weather analysis warning model establishment and judgment interface.

[0106] The radar electromagnetic wave scattering parameter analysis interface is used to input the electromagnetic wave scattering parameters of different cloud layer characteristic weather into the radar time series graph neural network detection algorithm for analysis and calculation to obtain the spectral width and corresponding polarization phase difference of different cloud layer characteristic weather.

[0107] The risk warning judgment interface for different weather types is used to judge the polarization parameter. When the fluctuation range of the polarization phase difference is higher than the safety limit range, a risk warning information for different weather types is issued.

[0108] Furthermore, the different weather type risk warning judgment interface is also used to obtain the current local meteorological information, calculate the occurrence period of the different cloud characteristic weather according to the meteorological information and polarization parameters, and issue different weather type risk warning information.

[0109] The electromagnetic wave scattering parameter acquisition interface is used to integrate the electromagnetic wave scattering parameter sets of different cloud layer characteristic weather conditions, including electromagnetic wave scattering parameters of different unit times. Each of the electromagnetic wave scattering parameters of the unit time includes the electromagnetic wave scattering parameters of a single different cloud layer characteristic weather condition and the corresponding cloud particle type of the different cloud layer characteristic weather condition. The cloud particle type corresponds to the echo intensity and polarization parameter of the different cloud layer characteristic weather conditions, wherein the polarization parameter includes the differential reflectivity of the different cloud layer characteristic weather conditions.

[0110] The weather analysis and warning model establishment and judgment interface is used to use the electromagnetic wave scattering parameter set, adopt a convolutional neural network model to optimize the radar time series graph neural network detection algorithm, and use this model to analyze the influencing factors of electromagnetic wave scattering parameters and judge the parameter boundary conditions.

[0111] In an embodiment of the present invention, the method for detecting different cloud layer characteristics may include an edge computing gateway server, a meteorological precipitation radar, and a weather data monitoring and management center. The edge computing gateway server and the meteorological precipitation radar exchange data via a network using 5G signals. The server performs statistical detection of electromagnetic wave scattering parameters transmitted by the meteorological precipitation radar and makes judgments based on the detection results. For example, when the polarization phase difference fluctuation range of different cloud layer characteristics exceeds a safe limit, a risk warning message for different weather types is issued. The weather data monitoring and management center can communicate with the edge computing gateway server via the network and analyze and calculate the electromagnetic wave scattering parameters for different cloud layer characteristics. Through the weather data monitoring and management center, weather management personnel can obtain information on the polarization parameters of different cloud layer characteristics in the monitored area, as well as risk warning information for different weather types, from the electromagnetic wave scattering parameters for different cloud layer characteristics collected by the meteorological precipitation radar. This method for detecting different cloud layer characteristics achieves automatic detection of different cloud layer characteristics and risk warnings for different weather types, improving the efficiency of detecting different cloud layer characteristics.

[0112] In this embodiment, the weather data collection and management center is an integrated system platform specifically designed to collect, store, process, and manage weather data, serving as a core hub for decision support in meteorological monitoring and forecasting. Its primary function is to acquire large amounts of meteorological data in real time through a variety of sensors (such as meteorological rainfall radars, satellites, and ground-based weather stations), and to centrally store, intelligently analyze, and distribute this data, enabling efficient meteorological monitoring and forecasting.

[0113] In this embodiment, the meteorological precipitation radar may include a first processor, a first data interaction interface, a first memory, and an electromagnetic wave scattering parameter collection interface. The first processor, first data interaction interface, first memory, and electromagnetic wave scattering parameter collection interface are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components may be electrically connected via one or more communication buses or signal lines. A meteorological precipitation radar is a high-tech meteorological device used to monitor and analyze the characteristics and distribution of atmospheric precipitation and is widely used in weather forecasting and disaster warning. It emits high-frequency electromagnetic waves and exploits the reflection characteristics of precipitation particles (such as raindrops, snowflakes, or hail) to capture the intensity, distribution, and dynamic changes of precipitation in real time. Based on the intensity, phase, and frequency offset of the echo signal, the radar can obtain key information such as precipitation reflectivity, particle velocity, and particle distribution, thereby analyzing the intensity, direction, and characteristics of the weather system. Doppler radar further measures the velocity of precipitation particles through the Doppler effect, while polarimetric radar uses the difference between horizontally and vertically polarized waves to distinguish precipitation type (e.g., rain, snow, hail) and particle shape. Meteorological precipitation radar offers the advantages of real-time performance, wide coverage, and high accuracy. It can rapidly monitor precipitation distribution and wind dynamics over large areas, playing a key role in providing early warnings for extreme weather such as heavy rain, hail, and thunderstorms. Although its close-range detection suffers from blind spots and signal attenuation at longer distances, these limitations are gradually being overcome through the integration of multi-source radar networks. As a crucial tool for modern meteorological observation, meteorological precipitation radar is widely used in precipitation monitoring, disaster prevention and mitigation, aviation safety, and water resource management, providing crucial technical support for improving the accuracy of weather forecasts.

[0114] The meteorological rainfall radar may also include a low-spurious local oscillator generation interface composed of a constant-temperature clock source, a low-spurious phase-locked loop (PLL), and an op amp; a frequency synthesis interface composed of a low-spurious phase-locked loop (PLL), a DAC, and a digital DDS; a transceiver link interface; and a data processing interface. Specifically, the low-spurious local oscillator generation interface generates a stable local oscillator signal; the frequency synthesis interface generates a high-purity intermediate frequency (IF) signal; the transmit link interface performs signal frequency boosting, amplification, and filtering, and outputs the signal to the transmit antenna; the receive link interface performs received signal amplification, filtering, frequency reduction, and digitization; and the data processing interface acquires, processes, and outputs the signal.

[0115] In this embodiment, the electromagnetic wave scattering parameter acquisition interface is a key device for capturing and analyzing the scattering characteristics of electromagnetic waves from target objects (such as precipitation particles, clouds, or the ground surface). It is widely used in fields such as meteorological detection, radar systems, and environmental monitoring. The interface transmits high-frequency electromagnetic waves, receives echo signals reflected by the target, and extracts scattering characteristic parameters such as reflectivity, polarization, phase difference, and Doppler shift. These parameters can reveal the size, shape, material, and motion of the target object, such as precipitation intensity and type (such as rain, snow, or hail) in meteorological applications, as well as particle distribution and movement direction. The acquisition interface typically includes a transmitter, a receiver, and a signal processing module. Using modern electronic technologies and algorithms, it filters, amplifies, and digitizes the echo signals, achieving high-precision data acquisition. Combined with a radar system, the electromagnetic wave scattering parameter acquisition interface can be used to monitor target characteristics in complex environments in real time, supporting weather forecasting, disaster prevention and mitigation, and atmospheric science research. Its advantages include strong real-time performance, high accuracy, and wide adaptability, but it can also be affected by terrain shielding, environmental noise, and multipath effects. With technological advancements, the interface has been continuously improved in detection range, sensitivity and data processing capabilities, becoming an indispensable core module in modern radar and meteorological monitoring systems.

[0116] A meteorological rainfall radar is established through the network and an edge computing gateway server uses 5G signals to interact with data, and sends and receives data through the network.

[0117] In this embodiment, the edge computing gateway server and the weather data set control and management center use 5G signals to exchange data. The edge computing gateway server may have a structure identical or similar to that of the weather data set control and management center, and may be used to statistically detect electromagnetic wave scattering parameters of weather with different cloud layer characteristics, predict the occurrence period of weather with different cloud layer characteristics, and issue different weather type risk warning information to the weather data set control and management center based on the occurrence period of polarization parameters corresponding to different cloud layer characteristics. Of course, in other embodiments, the method for detecting weather with different cloud layer characteristics may not include an edge computing gateway server, and the functions performed by the edge computing gateway server may be replaced by the weather data set control and management center, which will not be repeated here.

[0118] The present invention also provides a storage medium storing a computer program. When the computer program is run by a processor, the computer program executes the steps of the weather detection method based on the meteorological rainfall radar.

[0119] In summary, the present invention provides a weather detection method, system, and storage medium based on a meteorological rain measuring radar. The method uploads the electromagnetic wave scattering parameters of different cloud layer characteristic weather in the monitoring area collected by the meteorological rain measuring radar to an edge computing gateway server; the edge computing gateway server inputs the acquired electromagnetic wave scattering parameters of different cloud layer characteristic weather into the radar timing graph neural network detection algorithm for analysis and calculation to obtain the spectral width and corresponding polarization phase difference of different cloud layer characteristic weather; the polarization parameter is judged, and when the polarization phase difference fluctuation range is higher than the safety limit interval, different weather type risk warning information is issued. The method can automatically detect the spectral width and corresponding polarization phase difference of different cloud layer characteristic weather, thereby improving the accuracy and efficiency of detecting different cloud layer characteristic weather.

[0120] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A weather detection method based on a meteorological rain radar, characterized in that: The method includes: Step S1, using a meteorological rainfall radar to collect characteristic weather electromagnetic wave scattering parameters of different cloud layers in the monitoring area; Step S2, integrating electromagnetic wave scattering parameter sets for different cloud layer characteristic weather conditions, including electromagnetic wave scattering parameters for different unit times, cloud particle types for different cloud layer characteristic weather conditions corresponding to each electromagnetic wave scattering parameter for each unit time, echo intensities and polarization parameters corresponding to the cloud particle types for different cloud layer characteristic weather conditions, and differential reflectivities corresponding to the polarization parameters for different cloud layer characteristic weather conditions; Step S3, using the electromagnetic wave scattering parameter set, establishing a weather analysis and warning model for multimodal fusion weight analysis, and using the model to analyze the influencing factors of the electromagnetic wave scattering parameters and determine the boundary conditions of the parameters; Step S4: inputting the obtained electromagnetic wave scattering parameters of different cloud layer characteristic weather into the radar time series graph neural network detection algorithm for analysis and calculation to obtain the spectrum width and corresponding polarization phase difference of different cloud layer characteristic weather; Step S5: Combine the influencing factors of electromagnetic wave scattering parameters and the parameter boundary condition judgment results to perform polarization phase difference judgment in the big data model. If the polarization phase difference fluctuation range is higher than the safety limit range, different weather type risk warning information is issued, including: (a) A historical statistical model of polarization parameters. Based on the influencing factors of electromagnetic wave scattering parameters and the results of parameter boundary conditions, a statistical distribution model of polarization parameters is established to represent the parameter fluctuation range under certain weather conditions. Assume that the polarization parameter set is ,in For the The polarization parameter value of the observation; Calculate historical mean of polarization parameters and standard deviation : ； Determine the safe fluctuation range of a certain weather type based on historical data ,in It is a parameter that controls the safety range; (b) Real-time polarization phase difference fluctuation judgment, real-time polarization phase difference fluctuation range judgment formula: Assume that the polarization parameter value at the current moment is , to determine whether it exceeds the safety limit range: ； in, Indicates the degree of deviation of polarization parameters; Conditional judgment: ； when When , it means that the current polarization parameters are out of the safe range and an early warning needs to be triggered; The weather analysis and warning model of the multimodal fusion weight analysis is expressed as: ； in, Represents the weight of factors affecting different electromagnetic wave scattering parameters, Indicates the echo strength, represents the polarization parameter, Represents the differential reflectivity of weather with different cloud characteristics, represents parameter boundary conditions, Cloud particle characteristic parameter matrix, represents polarization error compensation, LN is layer normalization, Dropout( ) is the preset weight distribution calculation, represents the multi-head self-attention mechanism, , , They are weather embedding matrices of different cloud layer characteristics, corresponding to query, key and value, is the learnable matrix of multi-head self-attention at different unit times.

2. The weather detection method based on meteorological rain radar according to claim 1, characterized in that: The obtained electromagnetic wave scattering parameters of different cloud layer characteristic weather are input into the radar time series graph neural network detection algorithm for analysis and calculation, including statistically analyzing the echo ratios corresponding to the polarization parameters of different cloud layer characteristic weather in the monitoring area collected by the meteorological rain measuring radar within a predetermined time period.

3. The weather detection method based on meteorological rain radar according to claim 1, characterized in that: When the polarization phase difference fluctuation range is higher than the safety limit range, different weather type risk warning information is issued, including obtaining the cloud height, vertical distribution data and cloud structure changes and development data of the weather at different terrains and altitudes, calculating the alarm range of different cloud characteristic weather according to regional information and polarization parameters, and issuing different weather type risk warning information. The different weather type risk warning information includes rainfall probability, thunderstorm probability, climate temperature and humidity, haze probability and level, secondary comprehensive disaster probability and level, where different probabilities and levels correspond to different degrees of severe convective weather hazards.

4. The weather detection method based on meteorological rain radar according to claim 1, characterized in that: The method uses 5G signals to exchange data between an edge computing gateway server and a weather data control and management center. The edge computing gateway server is provided with a weather analysis and warning model with multimodal fusion weight analysis. A meteorological rain measuring radar is used to collect electromagnetic wave scattering parameters of weather with different cloud layer characteristics in a monitoring area. The steps of using the electromagnetic wave scattering parameters of weather with different cloud layer characteristics as obtained by a method for detecting weather with different cloud layer characteristics include: the meteorological rain measuring radar uploads the collected electromagnetic wave scattering parameters of weather with different cloud layer characteristics in a monitoring area to the edge computing gateway server.

5. The weather detection method based on meteorological rain radar according to claim 1, characterized in that: The step of inputting the obtained electromagnetic wave scattering parameters of weather with different cloud layer characteristics into the radar time series graph neural network detection algorithm for analysis and calculation includes: the edge computing gateway server inputs the obtained electromagnetic wave scattering parameters of weather with different cloud layer characteristics in the monitoring area into the radar time series graph neural network detection algorithm of the edge computing gateway server to analyze and calculate the electromagnetic wave scattering parameters of weather with different cloud layer characteristics in the monitoring area.

6. The weather detection system based on meteorological rain radar is characterized by: The method according to any one of claims 1 to 5 is implemented by a weather detection system based on a meteorological rain radar, the system comprising: The meteorological rainfall radar, the weather data set monitoring and management center that exchanges data with the meteorological rainfall radar, and the different weather type risk warning modules connected to the weather data set monitoring and management center use the meteorological rainfall radar to collect electromagnetic wave scattering parameters of weather with different cloud layer characteristics in the monitoring area, which are used as different cloud layer characteristic weather detection and different weather type risk warning methods to obtain electromagnetic wave scattering parameters of weather with different cloud layer characteristics; the weather data set monitoring and management center includes a radar time series graph neural network detection algorithm, and the different weather type risk warning modules include: The radar electromagnetic wave scattering parameter analysis interface is used to input the electromagnetic wave scattering parameters of different cloud layer characteristic weather into the radar time series graph neural network detection algorithm for analysis and calculation to obtain the spectrum width and corresponding polarization phase difference of different cloud layer characteristic weather; The initial information comprehensive pre-processing interface is used to extract features from the unit time data corresponding to different cloud layer characteristic weather conditions obtained from the radar electromagnetic wave scattering parameter analysis interface, perform denoising and clustering on the feature-extracted data, and transmit the clustered data to the risk warning judgment interface for different weather types; The risk warning interface for different weather types is used to combine the influencing factors of electromagnetic wave scattering parameters and the judgment results of parameter boundary conditions to perform polarization phase difference judgment in the big data model. If the polarization phase difference fluctuation range exceeds the safety limit, different weather type risk warning information will be issued, including: (a) A historical statistical model of polarization parameters. Based on the influencing factors of electromagnetic wave scattering parameters and the results of parameter boundary conditions, a statistical distribution model of polarization parameters is established to represent the parameter fluctuation range under certain weather conditions. Assume that the polarization parameter set is ,in For the The polarization parameter value of the observation; Calculate historical mean of polarization parameters and standard deviation : ； Determine the safe fluctuation range of a certain weather type based on historical data ,in It is a parameter that controls the safety range; (b) Real-time polarization phase difference fluctuation judgment, real-time polarization phase difference fluctuation range judgment formula: Assume that the polarization parameter value at the current moment is , to determine whether it exceeds the safety limit range: ； in, Indicates the degree of deviation of polarization parameters; Conditional judgment: ； when , it means that the current polarization parameters are out of the safe range and an early warning needs to be triggered.

7. The weather detection system based on meteorological rainfall radar according to claim 6, characterized in that: The different weather type risk warning modules also include: The electromagnetic wave scattering parameter acquisition interface is used to integrate the electromagnetic wave scattering parameter sets of different cloud layer characteristic weather conditions, including electromagnetic wave scattering parameters of different unit times. Each of the electromagnetic wave scattering parameters of the unit time includes the electromagnetic wave scattering parameter of a single different cloud layer characteristic weather condition and the corresponding cloud particle type of the different cloud layer characteristic weather condition. The cloud particle type corresponds to the echo intensity and polarization parameter of the different cloud layer characteristic weather conditions. The weather analysis and warning model establishment and judgment interface is used to use the electromagnetic wave scattering parameter set to establish a weather analysis and warning model for multimodal fusion weight analysis, and use the model to analyze the influencing factors of electromagnetic wave scattering parameters and judge the parameter boundary conditions.

8. The weather detection system based on meteorological rain radar according to claim 6, characterized in that: The risk warning judgment interface for different weather types is also used for: the height of the cloud layer, vertical distribution data, and structural changes and development data of the cloud layer in different terrains and altitudes, and calculating the cumulative number and incremental number of different cloud layer characteristic weather in the region based on regional information and polarization parameters, and issuing risk warning information for different weather types. The risk warning information for different weather types includes rainfall probability, thunderstorm probability, climate temperature and humidity, haze probability and level, secondary comprehensive disaster probability and level, where different probabilities and levels correspond to different degrees of severe convective weather hazards.

9. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the weather detection method based on meteorological rainfall radar as claimed in any one of claims 1 to 5 are executed.

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